Efficient Learning and Inference for High-dimensional Lagrangian Systems

Learning the nature of a physical system is a problem that presents many challenges and opportunities owing to the unique structure associated with such systems. Many physical systems of practical interest in engineering are high-dimensional, which prohibits the application of standard learning methods to such problems. This first part of this work proposes therefore to solve learning problems associated with physical systems by identifying their low-dimensional Lagrangian structure. Algorithms are given to learn this structure in the case that it is obscured by a change of coordinates. The associated inference problem corresponds to solving a high-dimensional minimum-cost path problem, which can be solved by exploiting the symmetry of the problem. These techniques are demonstrated via an application to learning from high-dimensional human motion capture data. The second part of this work is concerned with the application of these methods to high-dimensional motion planning. Algorithms are given to learn and exploit the struc- ture of holonomic motion planning problems effectively via spectral analysis and iterative dynamic programming, admitting solutions to problems of unprecedented dimension com- pared to known methods for optimal motion planning. The quality of solutions found is also demonstrated to be much superior in practice to those obtained via sampling-based planning and smoothing, in both simulated problems and experiments with a robot arm. This work therefore provides strong validation of the idea that learning low-dimensional structure is the key to future advances in this field

Deep Network Flow for Multi-Object Tracking

Data association problems are an important component of many computer vision applications, with multi-object tracking being one of the most prominent examples. A typical approach to data association involves finding a graph matching or network flow that minimizes a sum of pairwise association costs, which are often either hand-crafted or learned as linear functions of fixed features. In this work, we demonstrate that it is possible to learn features for network-flow-based data association via backpropagation, by expressing the optimum of a smoothed network flow problem as a differentiable function of the pairwise association costs. We apply this approach to multi-object tracking with a network flow formulation. Our experiments demonstrate that we are able to successfully learn all cost functions for the association problem in an end-to-end fashion, which outperform hand-crafted costs in all settings. The integration and combination of various sources of inputs becomes easy and the cost functions can be learned entirely from data, alleviating tedious hand-designing of costs.Comment: Accepted to CVPR 201

Learning random-walk label propagation for weakly-supervised semantic segmentation

Large-scale training for semantic segmentation is challenging due to the expense of obtaining training data for this task relative to other vision tasks. We propose a novel training approach to address this difficulty. Given cheaply-obtained sparse image labelings, we propagate the sparse labels to produce guessed dense labelings. A standard CNN-based segmentation network is trained to mimic these labelings. The label-propagation process is defined via random-walk hitting probabilities, which leads to a differentiable parameterization with uncertainty estimates that are incorporated into our loss. We show that by learning the label-propagator jointly with the segmentation predictor, we are able to effectively learn semantic edges given no direct edge supervision. Experiments also show that training a segmentation network in this way outperforms the naive approach.Comment: This is a revised version of a paper presented at CVPR 2017 that corrects some equations. See footnote

Learning to locate from demonstrated searches

Abstract—We consider the problem of learning to locate targets from demonstrated searches. In this concept, a human demonstrates tours of environments that are assumed to minimize the human’s expected time to locate the target, given the person’s latent prior over potential target locations. The latent prior is then learned as a function of environmental features, enabling a robot to search novel environments in a way that would be deemed efficient by the teacher. We present novel approaches to solve both the inference problem of planning an expected-time-optimal tour given a prior and the learning problem of deducing the prior from observed tours. Our learning algorithm is inspired by and similar to maximum margin planning (MMP), although it differs in key ways. On the inference side, we advance the state-of-the-art by proposing novel relaxations that are integrated into a heuristic-driven search algorithm. An application to a home assistant scenario is discussed, and experimental results are given validating our methods in this domain. I

Proprioceptive localization for a quadrupedal robot on known terrain

We present a novel method for the localization of a legged robot on known terrain using only proprioceptive sensors such as joint encoders and an inertial measurement unit. In contrast to other proprioceptive pose estimation techniques, this method allows for global localization (i.e., localization with large initial uncertainty) without the use of exteroceptive sensors. This is made possible by establishing a measurement model based on the feasibility of putative poses on known terrain given observed joint angles and attitude measurements. Results are shown that demonstrate that the method performs better than dead-reckoning, and is also able to perform global localization from large initial uncertainty

Robust GPS/INS-Aided Localization and Mapping via GPS Bias Estimation

Summary. We consider the problem of pose estimation in the context of outdoor robotic mapping. In such cases absolute position information from GPS is often available, making a full-blown SLAM implementation largely unnecessary. However, the peculiarities of GPS can lead to problems when using it in conjunction with a naive mapping system, as unpredictable biases tend to cause significant inconsistencies in the generated maps. We thus present a two-stage pose estimation system to address this problem. The first stage consists of a best-effort "blind" pose estimator based on a robust and extensible Rao-Blackwellized particle filtering framework. The estimate from this stage is then fed to a "seeing" HMM-style filter that attempts to infer the uncorrected bias of the first stage by matching stereo maps under an assumption of scene rigidity. Results are shown that demonstrate a vast improvement in pose estimates and map consistency using this method over the naive approach

Search-based Planning for a Legged Robot over Rough Terrain

We present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains

Symmetries and asymmetries associated with non-random segregation of sister DNA strands in Escherichia coli

AbstractThe successful inheritance of genetic information across generations is a complex process requiring replication of the genome and its faithful segregation into two daughter cells. At each replication cycle there is a risk that new DNA strands incorporate genetic changes caused by miscopying of parental information. By contrast the parental strands retain the original information. This raises the intriguing possibility that specific cell lineages might inherit “immortal” parental DNA strands via non-random segregation. If so, this requires an understanding of the mechanisms of non-random segregation. Here, we review several aspects of asymmetry in the very symmetrical cell, Escherichia coli, in the interest of exploring the potential basis for non-random segregation of leading- and lagging-strand replicated chromosome arms. These considerations lead us to propose a model for DNA replication that integrates chromosome segregation and genomic localisation with non-random strand segregation